Best Practices in Convolutional Networks for Forward-Looking Sonar Image Recognition

Convolutional Neural Networks (CNN) have revolutionized perception for color images, and their application to sonar images has also obtained good results. But in general CNNs are difficult to train without a large dataset, need manual tuning of a considerable number of hyperparameters, and require many careful decisions by a designer. In this work, we evaluate three common decisions that need to be made by a CNN designer, namely the performance of transfer learning, the effect of object/image size and the relation between training set size. We evaluate three CNN models, namely one based on LeNet, and two based on the Fire module from SqueezeNet. Our findings are: Transfer learning with an SVM works very well, even when the train and transfer sets have no classes in common, and high classification performance can be obtained even when the target dataset is small. The ADAM optimizer combined with Batch Normalization can make a high accuracy CNN classifier, even with small image sizes (16 pixels). At least 50 samples per class are required to obtain $90\%$ test accuracy, and using Dropout with a small dataset helps improve performance, but Batch Normalization is better when a large dataset is available.Comment: Author version; IEEE/MTS Oceans 2017 Aberdee

Improving Sonar Image Patch Matching via Deep Learning

Matching sonar images with high accuracy has been a problem for a long time, as sonar images are inherently hard to model due to reflections, noise and viewpoint dependence. Autonomous Underwater Vehicles require good sonar image matching capabilities for tasks such as tracking, simultaneous localization and mapping (SLAM) and some cases of object detection/recognition. We propose the use of Convolutional Neural Networks (CNN) to learn a matching function that can be trained from labeled sonar data, after pre-processing to generate matching and non-matching pairs. In a dataset of 39K training pairs, we obtain 0.91 Area under the ROC Curve (AUC) for a CNN that outputs a binary classification matching decision, and 0.89 AUC for another CNN that outputs a matching score. In comparison, classical keypoint matching methods like SIFT, SURF, ORB and AKAZE obtain AUC 0.61 to 0.68. Alternative learning methods obtain similar results, with a Random Forest Classifier obtaining AUC 0.79, and a Support Vector Machine resulting in AUC 0.66.Comment: Author versio

Sub-Ensembles for Fast Uncertainty Estimation in Neural Networks

Fast estimates of model uncertainty are required for many robust robotics applications. Deep Ensembles provides state of the art uncertainty without requiring Bayesian methods, but still it is computationally expensive due to the use of large ensembles. In this paper we propose deep sub-ensembles, an approximation to deep ensembles where the core idea is to ensemble only a selection of layers close to the output, and not the whole model. This is motivated by feature hierarchy learned by convolutional networks that should allow for feature reuse across ensembles. With ResNet-20 on the CIFAR10 dataset, we obtain 1.5-2.5 speedup over a deep ensemble, with a small increase in error and loss, and similarly up to 5-15 speedup with a VGG-like network on the SVHN dataset. Our results show that this idea enables a trade-off between error and uncertainty quality versus computational performance as a sub-ensemble effectively works as an approximation of a deep ensemble

Sub-Ensembles for Fast Uncertainty Estimation in Neural Networks

Fast estimates of model uncertainty are required for many robust robotics applications. Deep Ensembles provides state of the art uncertainty without requiring Bayesian methods, but still it is computationally expensive due to the use of large ensembles. In this paper we propose deep sub-ensembles, an approximation to deep ensembles where the core idea is to ensemble only a selection of layers close to the output, and not the whole model. This is motivated by feature hierarchy learned by convolutional networks that should allow for feature reuse across ensembles. With ResNet-20 on the CIFAR10 dataset, we obtain 1.5-2.5 speedup over a deep ensemble, with a small increase in error and loss, and similarly up to 5-15 speedup with a VGG-like network on the SVHN dataset. Our results show that this idea enables a trade-off between error and uncertainty quality versus computational performance as a sub-ensemble effectively works as an approximation of a deep ensemble

Sub-Ensembles for Fast Uncertainty Estimation in Neural Networks

Fast estimates of model uncertainty are required for many robust robotics applications. Deep Ensembles provides state of the art uncertainty without requiring Bayesian methods, but still it is computationally expensive due to the use of large ensembles. In this paper we propose deep sub-ensembles, an approximation to deep ensembles where the core idea is to ensemble only a selection of layers close to the output, and not the whole model. This is motivated by feature hierarchy learned by convolutional networks that should allow for feature reuse across ensembles. With ResNet-20 on the CIFAR10 dataset, we obtain 1.5-2.5 speedup over a deep ensemble, with a small increase in error and loss, and similarly up to 5-15 speedup with a VGG-like network on the SVHN dataset. Our results show that this idea enables a trade-off between error and uncertainty quality versus computational performance as a sub-ensemble effectively works as an approximation of a deep ensemble